Storage of boar semen at 16-18 °C in the long-term commercial extender prepared with deionized water or nanowater
Bibliographic record
Abstract
The aim of this study was to assess the usefulness of nanowater (NW; water declusterized using cold plasma treatment) as a diluent for a commercial boar semen extender during the 15-day storage (Days 1 to 15) at 16-18 °C.Ejaculates collected from 8 boars were subjected to the standard evaluation and then diluted in the extender prepared with deionized water (DW) or NW to a final concentration of 3×10 9 spermatozoa/ml.The proportion of defective spermatozoa increased (P<0.05) from Day 10 to Day 15 of storage (22.8±16.6% to 41.8±26.4% in DW group and 18.6±11.7%to 34.8±25.4% in NW group) and it was significantly greater in DW group compared with NW group on Days 5 and 10 due mainly to a greater (P<0.05)number of mid-piece defects in semen stored in the DW-containing extender.Sperm progressive motility decreased (P<0.05) in both groups between Days 2 and 6, Days 6 and 10, and Days 10 and 12, whereas the percentage of motile spermatozoa declined (P<0.05) to Day 14 only in NW group.Sperm motility was greater (P<0.05) in NW group compared with DW group from Day 5 to Day 13.A decline in sperm progressive motility below 40% in all semen samples occurred by Day 11 in DW group and by Day 12 in NW group.The mean survival time of sperm at 37 °C ex situ was greater in NW group than in DW group on Day 5 (314±87 min compared with 284±87 min) and Day 10 (223±34 min compared with 182±27 min; NW group compared with DW group, respectively).There were no differences (P>0.05) between the two groups in the concentrations of alkaline phosphatase and aspartate aminotransferase in semen extender.To summarize, the use of NW as an extender diluent exerts cytoprotective effects on boar spermatozoa and delays a decline in sperm progressive motility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".